Consistency of Causal Inference under the Additive Noise Model
نویسندگان
چکیده
We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods consistently infers the causal direction in a nonparametric setting.
منابع مشابه
Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise
The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive noise model through minimization of an estimator of the squaredloss mutual information b...
متن کاملEntropic Causal Inference
We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam’s razor, we assume that the exogenous variable is simple in the true causal direction. We quantify simplicity using Rényi entropy....
متن کاملTelling cause from effect in deterministic linear dynamical systems
Inferring a cause from its effect using observed time series data is a major challenge in naturaland social sciences. Assuming the effect is generated by the cause trough a linear system,we propose a new approach based on the hypothesis that nature chooses the “cause” and the“mechanism that generates the effect from the cause” independent of each other. We thereforepostulate...
متن کاملDistinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y . An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variab...
متن کاملJustifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory
A recent method for causal discovery is in many cases able to infer whether X causes Y or Y causes X for just two observed variables X and Y . It is based on the observation that there exist (non-Gaussian) joint distributions P (X,Y ) for which Y may be written as a function of X up to an additive noise term that is independent of X and no such model exists from Y to X . Whenever this is the ca...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014